Training flood modelling Porto Alegre
by Netherlands Enterprise AgencyBrazil
Imported from IATI · publisher data as of Oct 1, 2026
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Budget
€49,938
Timeline
Sep 30, 2026 – Jul 31, 2027
About this project
In 2024, the state of Rio Grande do Sul experienced one of the most severe flood events in Brazil’s history, with extreme rainfall causing widespread flooding, significant damage, and major disruptions to communities and infrastructure. Porto Alegre, the state capital, was among the most affected areas. Currently, the Municipal Department of Water and Sewage of Porto Alegre (DMAE) is developing flood adaptation strategies for several river basins. However, there are limitations with their modelling approaches in use, which affects their capability of making comprehensive decisions on solutions for flood prevention.After attending a training on modelling of flood risks and hazards in Rio De Janeiro, provided by Deltares, DMAE requested Deltares for a similar training to support with their to support in better understanding the flood risk in Porto Alegre’s basins and to develop adaptation strategies to mitigate the flood risk.This workshop is based on 2 of Deltares’ modelling tools (SFINCS and FloodAdapt) and will allow more accurate mapping of water depths and flood extents, particularly in areas where water cannot enter the drainage system or where backwater effects dominate. These models integrate 2D hydrodynamics with impact and risk modelling, so they will further extend the analysis beyond hydraulic performance to include exposure of people, infrastructure, and assets, enabling quantitative damage assessment and prioritisation of interventions.The training consists of 3…
Published details
View source- Published by
- Netherlands Enterprise Agency · Government
- Countries
- Brazil
- Sectors
- Water sector policy and administrative managementRiver basins developmentDisaster Risk Reduction
- Funded by
- Netherlands Enterprise Agency
- Accountable
- Netherlands Enterprise Agency
Published by Netherlands Enterprise Agency to the International Aid Transparency Initiative (IATI), last updated 1 Oct 2026 and imported automatically. IATI identifier: NL-KVK-27378529-PVW526BR01.
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